




'''simple docstring'''







from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowerCAmelCase_     						=				logging.get_logger(__name__)

# General docstring
lowerCAmelCase_     						=				'''ResNetConfig'''

# Base docstring
lowerCAmelCase_     						=				'''microsoft/resnet-50'''
lowerCAmelCase_     						=				[1, 2_0_4_8, 7, 7]

# Image classification docstring
lowerCAmelCase_     						=				'''microsoft/resnet-50'''
lowerCAmelCase_     						=				'''tiger cat'''

lowerCAmelCase_     						=				[
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class 	_snake_case(			nn.Module      ):






 def __init__(self       :  Union[str, Any]    ,		a       :  int    ,		a       :  int    ,		a       :  int = 3    ,		a       :  int = 1    ,		a       :  str = "relu"    )       ->   Any:






    """simple docstring"""


    super().__init__()
    A__       =     nn.Convad(
        a    ,		a    ,		kernel_size=a    ,		stride=a    ,		padding=kernel_size // 2    ,		bias=a    )
    A__       =     nn.BatchNormad(a    )
    A__       =     ACTaFN[activation] if activation is not None else nn.Identity()






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  Tensor    )       ->   Tensor:






    """simple docstring"""


    A__       =     self.convolution(a    )
    A__       =     self.normalization(a    )
    A__       =     self.activation(a    )
    return hidden_state




class 	_snake_case(			nn.Module      ):






 def __init__(self       :  Dict    ,		a       :  ResNetConfig    )       ->   List[Any]:






    """simple docstring"""


    super().__init__()
    A__       =     ResNetConvLayer(
        config.num_channels    ,		config.embedding_size    ,		kernel_size=7    ,		stride=2    ,		activation=config.hidden_act    )
    A__       =     nn.MaxPoolad(kernel_size=3    ,		stride=2    ,		padding=1    )
    A__       =     config.num_channels






 def 						_UpperCamelCase (self       :  Optional[Any]    ,		a       :  Tensor    )       ->   Tensor:






    """simple docstring"""


    A__       =     pixel_values.shape[1]
    if num_channels != self.num_channels:
       raise ValueError(
           'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'    )
    A__       =     self.embedder(a    )
    A__       =     self.pooler(a    )
    return embedding




class 	_snake_case(			nn.Module      ):






 def __init__(self       :  Union[str, Any]    ,		a       :  int    ,		a       :  int    ,		a       :  int = 2    )       ->   Any:






    """simple docstring"""


    super().__init__()
    A__       =     nn.Convad(a    ,		a    ,		kernel_size=1    ,		stride=a    ,		bias=a    )
    A__       =     nn.BatchNormad(a    )






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  Tensor    )       ->   Tensor:






    """simple docstring"""


    A__       =     self.convolution(a    )
    A__       =     self.normalization(a    )
    return hidden_state




class 	_snake_case(			nn.Module      ):






 def __init__(self       :  Optional[int]    ,		a       :  int    ,		a       :  int    ,		a       :  int = 1    ,		a       :  str = "relu"    )       ->   int:






    """simple docstring"""


    super().__init__()
    A__       =     in_channels != out_channels or stride != 1
    A__       =     (
        ResNetShortCut(a    ,		a    ,		stride=a    ) if should_apply_shortcut else nn.Identity()
    )
    A__       =     nn.Sequential(
        ResNetConvLayer(a    ,		a    ,		stride=a    )    ,		ResNetConvLayer(a    ,		a    ,		activation=a    )    ,		)
    A__       =     ACTaFN[activation]






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  List[str]    )       ->   int:






    """simple docstring"""


    A__       =     hidden_state
    A__       =     self.layer(a    )
    A__       =     self.shortcut(a    )
    hidden_state += residual
    A__       =     self.activation(a    )
    return hidden_state




class 	_snake_case(			nn.Module      ):






 def __init__(self       :  Tuple    ,		a       :  int    ,		a       :  int    ,		a       :  int = 1    ,		a       :  str = "relu"    ,		a       :  int = 4    )       ->   Optional[Any]:






    """simple docstring"""


    super().__init__()
    A__       =     in_channels != out_channels or stride != 1
    A__       =     out_channels // reduction
    A__       =     (
        ResNetShortCut(a    ,		a    ,		stride=a    ) if should_apply_shortcut else nn.Identity()
    )
    A__       =     nn.Sequential(
        ResNetConvLayer(a    ,		a    ,		kernel_size=1    )    ,		ResNetConvLayer(a    ,		a    ,		stride=a    )    ,		ResNetConvLayer(a    ,		a    ,		kernel_size=1    ,		activation=a    )    ,		)
    A__       =     ACTaFN[activation]






 def 						_UpperCamelCase (self       :  Union[str, Any]    ,		a       :  Any    )       ->   str:






    """simple docstring"""


    A__       =     hidden_state
    A__       =     self.layer(a    )
    A__       =     self.shortcut(a    )
    hidden_state += residual
    A__       =     self.activation(a    )
    return hidden_state




class 	_snake_case(			nn.Module      ):






 def __init__(self       :  str    ,		a       :  ResNetConfig    ,		a       :  int    ,		a       :  int    ,		a       :  int = 2    ,		a       :  int = 2    ,		)       ->   Optional[Any]:






    """simple docstring"""


    super().__init__()

    A__       =     ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

    A__       =     nn.Sequential(
        # downsampling is done in the first layer with stride of 2
        layer(a    ,		a    ,		stride=a    ,		activation=config.hidden_act    )    ,		*[layer(a    ,		a    ,		activation=config.hidden_act    ) for _ in range(depth - 1    )]    ,		)






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Tensor    )       ->   Tensor:






    """simple docstring"""


    A__       =     input
    for layer in self.layers:
       A__       =     layer(a    )
    return hidden_state




class 	_snake_case(			nn.Module      ):






 def __init__(self       :  int    ,		a       :  ResNetConfig    )       ->   int:






    """simple docstring"""


    super().__init__()
    A__       =     nn.ModuleList([]    )
    # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
    self.stages.append(
        ResNetStage(
            a    ,		config.embedding_size    ,		config.hidden_sizes[0]    ,		stride=2 if config.downsample_in_first_stage else 1    ,		depth=config.depths[0]    ,		)    )
    A__       =     zip(config.hidden_sizes    ,		config.hidden_sizes[1:]    )
    for (in_channels, out_channels), depth in zip(a    ,		config.depths[1:]    ):
       self.stages.append(ResNetStage(a    ,		a    ,		a    ,		depth=a    )    )






 def 						_UpperCamelCase (self       :  Any    ,		a       :  Tensor    ,		a       :  bool = False    ,		a       :  bool = True    )       ->   BaseModelOutputWithNoAttention:






    """simple docstring"""


    A__       =     () if output_hidden_states else None

    for stage_module in self.stages:
       if output_hidden_states:
          A__       =     hidden_states + (hidden_state,)

       A__       =     stage_module(a    )

    if output_hidden_states:
       A__       =     hidden_states + (hidden_state,)

    if not return_dict:
       return tuple(v for v in [hidden_state, hidden_states] if v is not None    )

    return BaseModelOutputWithNoAttention(
        last_hidden_state=a    ,		hidden_states=a    ,		)




class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Optional[int]						=					ResNetConfig
 __snake_case:							List[Any]						=					'''resnet'''
 __snake_case:							Tuple						=					'''pixel_values'''
 __snake_case:							str						=					True






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Union[str, Any]    )       ->   str:






    """simple docstring"""


    if isinstance(a    ,		nn.Convad    ):
       nn.init.kaiming_normal_(module.weight    ,		mode='fan_out'    ,		nonlinearity='relu'    )
    elif isinstance(a    ,		(nn.BatchNormad, nn.GroupNorm)    ):
       nn.init.constant_(module.weight    ,		1    )
       nn.init.constant_(module.bias    ,		0    )






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  int    ,		a       :  Tuple=False    )       ->   List[str]:






    """simple docstring"""


    if isinstance(a    ,		a    ):
       A__       =     value




lowerCAmelCase_     						=				R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

lowerCAmelCase_     						=				R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''

@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''       , UpperCAmelCase       , )
class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Optional[int]    ,		a       :  Tuple    )       ->   str:






    """simple docstring"""


    super().__init__(a    )
    A__       =     config
    A__       =     ResNetEmbeddings(a    )
    A__       =     ResNetEncoder(a    )
    A__       =     nn.AdaptiveAvgPoolad((1, 1)    )
    # Initialize weights and apply final processing
    self.post_init()






 @add_start_docstrings_to_model_forward(a    )
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC    ,		output_type=a    ,		config_class=_CONFIG_FOR_DOC    ,		modality='vision'    ,		expected_output=_EXPECTED_OUTPUT_SHAPE    ,		)
 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  Tensor    ,		a       :  Optional[bool] = None    ,		a       :  Optional[bool] = None    )       ->   BaseModelOutputWithPoolingAndNoAttention:






    """simple docstring"""


    A__       =     (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    A__       =     return_dict if return_dict is not None else self.config.use_return_dict

    A__       =     self.embedder(a    )

    A__       =     self.encoder(
        a    ,		output_hidden_states=a    ,		return_dict=a    )

    A__       =     encoder_outputs[0]

    A__       =     self.pooler(a    )

    if not return_dict:
       return (last_hidden_state, pooled_output) + encoder_outputs[1:]

    return BaseModelOutputWithPoolingAndNoAttention(
        last_hidden_state=a    ,		pooler_output=a    ,		hidden_states=encoder_outputs.hidden_states    ,		)




@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''       , UpperCAmelCase       , )
class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Union[str, Any]    ,		a       :  Union[str, Any]    )       ->   List[str]:






    """simple docstring"""


    super().__init__(a    )
    A__       =     config.num_labels
    A__       =     ResNetModel(a    )
    # classification head
    A__       =     nn.Sequential(
        nn.Flatten()    ,		nn.Linear(config.hidden_sizes[-1]    ,		config.num_labels    ) if config.num_labels > 0 else nn.Identity()    ,		)
    # initialize weights and apply final processing
    self.post_init()






 @add_start_docstrings_to_model_forward(a    )
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT    ,		output_type=a    ,		config_class=_CONFIG_FOR_DOC    ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,		)
 def 						_UpperCamelCase (self       :  Any    ,		a       :  Optional[torch.FloatTensor] = None    ,		a       :  Optional[torch.LongTensor] = None    ,		a       :  Optional[bool] = None    ,		a       :  Optional[bool] = None    ,		)       ->   ImageClassifierOutputWithNoAttention:






    """simple docstring"""


    A__       =     return_dict if return_dict is not None else self.config.use_return_dict

    A__       =     self.resnet(a    ,		output_hidden_states=a    ,		return_dict=a    )

    A__       =     outputs.pooler_output if return_dict else outputs[1]

    A__       =     self.classifier(a    )

    A__       =     None

    if labels is not None:
       if self.config.problem_type is None:
          if self.num_labels == 1:
             A__       =     'regression'
          elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
             A__       =     'single_label_classification'
          else:
             A__       =     'multi_label_classification'
       if self.config.problem_type == "regression":
          A__       =     MSELoss()
          if self.num_labels == 1:
             A__       =     loss_fct(logits.squeeze()    ,		labels.squeeze()    )
          else:
             A__       =     loss_fct(a    ,		a    )
       elif self.config.problem_type == "single_label_classification":
          A__       =     CrossEntropyLoss()
          A__       =     loss_fct(logits.view(-1    ,		self.num_labels    )    ,		labels.view(-1    )    )
       elif self.config.problem_type == "multi_label_classification":
          A__       =     BCEWithLogitsLoss()
          A__       =     loss_fct(a    ,		a    )

    if not return_dict:
       A__       =     (logits,) + outputs[2:]
       return (loss,) + output if loss is not None else output

    return ImageClassifierOutputWithNoAttention(loss=a    ,		logits=a    ,		hidden_states=outputs.hidden_states    )




@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''       , UpperCAmelCase       , )
class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase      ):






 def __init__(self       :  List[str]    ,		a       :  List[str]    )       ->   List[str]:






    """simple docstring"""


    super().__init__(a    )
    super()._init_backbone(a    )

    A__       =     [config.embedding_size] + config.hidden_sizes
    A__       =     ResNetEmbeddings(a    )
    A__       =     ResNetEncoder(a    )

    # initialize weights and apply final processing
    self.post_init()






 @add_start_docstrings_to_model_forward(a    )
 @replace_return_docstrings(output_type=a    ,		config_class=_CONFIG_FOR_DOC    )
 def 						_UpperCamelCase (self       :  Any    ,		a       :  Tensor    ,		a       :  Optional[bool] = None    ,		a       :  Optional[bool] = None    )       ->   BackboneOutput:






    """simple docstring"""


    A__       =     return_dict if return_dict is not None else self.config.use_return_dict
    A__       =     (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )

    A__       =     self.embedder(a    )

    A__       =     self.encoder(a    ,		output_hidden_states=a    ,		return_dict=a    )

    A__       =     outputs.hidden_states

    A__       =     ()
    for idx, stage in enumerate(self.stage_names    ):
       if stage in self.out_features:
          feature_maps += (hidden_states[idx],)

    if not return_dict:
       A__       =     (feature_maps,)
       if output_hidden_states:
          output += (outputs.hidden_states,)
       return output

    return BackboneOutput(
        feature_maps=a    ,		hidden_states=outputs.hidden_states if output_hidden_states else None    ,		attentions=a    ,		)








'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     []
   for i in range(config.num_hidden_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('cls_token', 'deit.embeddings.cls_token'),
           ('dist_token', 'deit.embeddings.distillation_token'),
           ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
           ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
           ('pos_embed', 'deit.embeddings.position_embeddings'),
       ]						)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ('norm.weight', 'layernorm.weight'),
              ('norm.bias', 'layernorm.bias'),
              ('pre_logits.fc.weight', 'pooler.dense.weight'),
              ('pre_logits.fc.bias', 'pooler.dense.bias'),
          ]						)

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A__       =     [(pair[0], pair[1][4:]) if pair[1].startswith('deit'						) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ('norm.weight', 'deit.layernorm.weight'),
              ('norm.bias', 'deit.layernorm.bias'),
              ('head.weight', 'cls_classifier.weight'),
              ('head.bias', 'cls_classifier.bias'),
              ('head_dist.weight', 'distillation_classifier.weight'),
              ('head_dist.bias', 'distillation_classifier.bias'),
          ]						)

   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   for i in range(config.num_hidden_layers						):
      if base_model:
         A__       =     ''
      else:
         A__       =     'deit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[
          : config.hidden_size, :
      ]
      A__       =     in_proj_bias[: config.hidden_size]
      A__       =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A__       =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A__       =     in_proj_weight[
          -config.hidden_size :, :
      ]
      A__       =     in_proj_bias[-config.hidden_size :]







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)
   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     DeiTConfig()
   # all deit models have fine-tuned heads
   A__       =     False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A__       =     1000
   A__       =     'huggingface/label-files'
   A__       =     'imagenet-1k-id2label.json'
   A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
   A__       =     {int(UpperCAmelCase						): v for k, v in idalabel.items()}
   A__       =     idalabel
   A__       =     {v: k for k, v in idalabel.items()}
   A__       =     int(deit_name[-6:-4]						)
   A__       =     int(deit_name[-3:]						)
   # size of the architecture
   if deit_name[9:].startswith('tiny'						):
      A__       =     192
      A__       =     768
      A__       =     12
      A__       =     3
   elif deit_name[9:].startswith('small'						):
      A__       =     384
      A__       =     1536
      A__       =     12
      A__       =     6
   if deit_name[9:].startswith('base'						):
      pass
   elif deit_name[4:].startswith('large'						):
      A__       =     1024
      A__       =     4096
      A__       =     24
      A__       =     16

   # load original model from timm
   A__       =     timm.create_model(UpperCAmelCase      ,pretrained=UpperCAmelCase						)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A__       =     timm_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase      ,UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
   read_in_q_k_v(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # load HuggingFace model
   A__       =     DeiTForImageClassificationWithTeacher(UpperCAmelCase						).eval()
   model.load_state_dict(UpperCAmelCase						)

   # Check outputs on an image, prepared by DeiTImageProcessor
   A__       =     int(
       (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A__       =     DeiTImageProcessor(size=UpperCAmelCase      ,crop_size=config.image_size						)
   A__       =     image_processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     encoding['pixel_values']
   A__       =     model(UpperCAmelCase						)

   A__       =     timm_model(UpperCAmelCase						)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(UpperCAmelCase      ,outputs.logits      ,atol=1e-3						)

   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(UpperCAmelCase						)
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
   image_processor.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--deit_name''',
        default='''vit_deit_base_distilled_patch16_224''',
        type=str,
        help='''Name of the DeiT timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    lowerCAmelCase_     						=				parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


